Description Usage Arguments Value References See Also Examples
fill.SVT
is an iterative updating scheme for Nuclear Norm Minimization problem. An unconstrained
parahrase of the problem introduced in fill.nuclear
is
\mathrm{minimize}\quad \frac{1}{2}\|P_{Ω}(X-A) \|_F^2 + λ \| X \|_*
where P_{Ω}(X)=X_{ij} if it is observed, or 0 otherwise. It performs iterative shrinkage on newly computed singular values.
1 | fill.SVT(A, lambda = 1, maxiter = 100, tol = 0.001)
|
A |
an (n\times p) partially observed matrix. |
lambda |
a regularization parameter. |
maxiter |
maximum number of iterations to be performed. |
tol |
stopping criterion for an incremental progress. |
a named list containing
an (n\times p) matrix after completion.
cai_singular_2010filling
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## Not run:
## load image data of 'lena128'
data(lena128)
## transform 5% of entries into missing
A <- aux.rndmissing(lena128, x=0.05)
## apply the method
fill1 <- fill.SVT(A, lambda=0.1)
fill2 <- fill.SVT(A, lambda=1.0)
fill3 <- fill.SVT(A, lambda=20)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(A, col=gray((0:100)/100), axes=FALSE, main="5% missing")
image(fill1$X, col=gray((0:100)/100), axes=FALSE, main="lbd=0.1")
image(fill2$X, col=gray((0:100)/100), axes=FALSE, main="lbd=1")
image(fill3$X, col=gray((0:100)/100), axes=FALSE, main="lbd=10")
par(opar)
## End(Not run)
|
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